Generalised clusterwise regression for simultaneous estimation of optimal pavement clusters and performance models

References

• Abaza, K.A., 2004. Deterministic performance prediction model for rehabilitation and management of flexible pavement. International Journal of Pavement Engineering, 5 (2), 111–121. doi:10.1080/10298430412331286977.
   Google Scholar
• Aguiar-Moya, J.P., and Prozzi, J., 2011. Development Of Reliable Pavement Models. Report No. SWUTC/11/161025-1, Texas Transportation Institute Texas, A&M University System, Texas. Available from: http://static.tti.tamu.edu/swutc.tamu.edu/publications/technicalreports/161025-1.pdf [Accessed 4 December 2016).
   Google Scholar
• Ali, O., 2005. Evaluation of the Mechanistic Empirical Pavement Design Guide (NCHRP 1- 37 A). National Research Council Canada. doi:http://doi.org/10.4224/20377776.
   Google Scholar
• Attoh-Okine, N., 1999. Analysis of learning rate and momentum term in backpropagation neural network algorithm trained to predict pavement performance, Advances in Engineering Software, 30 (4), 291–302.
   Web of Science ®Google Scholar
• Attoh-Okine, N., and Adarkwa, O., 2013. Pavement condition surveys –overview of current practices. Delaware Center for Transportation, 245, 1–66.
   Google Scholar
• Attoh-Okine, N., Coogerb, K., and Mensaha, S., 2009. Multivariate adaptive regression (MARS) and hinged hyperplanes (HHP) for doweled pavement performance modelling, Construction and Building Materials, 23 (9), 3020–3023.
   Web of Science ®Google Scholar
• Bardaka, E., Labi, S., and Haddock, J.E., 2014. Using Enhanced Econometric Techniques to Verify the Service Life of Asset Intervention A Case Study for Indiana. Transportation Research Record 2431, Transportation Research Board, Washington, DC, 16–23.
   Google Scholar
• Bekheet, W., et al., 2008. Comparison between probabilistic and deterministic pavement management analysis: a case study for Arizona DOT. In: 7th International Conference on Managing Pavement Assets, 23–28 June 2008 Alberta, Canada.
   Google Scholar
• Chan, P., Oppermann, M., and Wu, S.S., 1997. North Carolina's Experience in Development of Pavement Performance Prediction and Modeling.  Transportation Research Record: Journal of the Transportation Research Board, 1592, 80–88. doi:10.3141/1592-10.
   Google Scholar
• Chen, D., et al., 2014. Development and Validation of Pavement Deterioration Models and Analysis Weight Factors for the NCDOT Pavement Management System. FHWA/NC/2011-01_Phse II, NCDOT, Raleigh, NC. Available from: https://connect.ncdot.gov/projects/planning/RNAProjDocs/2011-01-Phase%20II_Final%20Report.pdf. [Accessed 7 October 2017].
   Google Scholar
• Chen, D., and Mastin, N., 2016. Sigmoidal models for predicting pavement performance conditions. Journal of Performance of Constructed Facilities., 30 (4), 04015078. doi:10.1061/(ASCE)CF.1943-5509.0000833.
   Web of Science ®Google Scholar
• Darter, M.I., 1980. Requirements for Reliable Predictive Pavement Models. Transportation Research Record 766, Transportation Research Board, Washington, DC, 25–31.
   Google Scholar
• de Melo Silva, F., et al., 2000. Proposed Pavement Performance Models for Local Government Agencies in Michigan. Transportation Research Record 1699, Transportation Research Board, Washington, DC, 81–86. doi:10.3141/1699-11.
   Google Scholar
• DeSarbo, W.S., and Cron, W.L., 1988. A maximum likelihood methodology for clusterwise linear regression. Journal of Classification, 5, 249–282.
   Web of Science ®Google Scholar
• DeSarbo, W.S., Oliver, R.L., and Rangaswamy, A., 1989. A simulated annealing methodology for clusterwise linear regression. Psychometrika, 54 (4), 707–736.
   Web of Science ®Google Scholar
• Efron, B., 1979. Bootstrap methods: another look at the jackknife, Annals of Statistics, 7(1), 1–26.
   Web of Science ®Google Scholar
• Efron, B., and Tibshirani, R.J., 1993. An introduction to the bootstrap. Chapman and Hall, New York.
   Google Scholar
• Fox, J., and Monette, G., 1992. Generalized collinearity diagnostics, Journal of the American Statistical Association, 87 (417), 178–183.
   Web of Science ®Google Scholar
• Garcia-Diaz, A., and Riggins, M., 1984. Serviceability and Distress Methodology for Predicting Pavement Performance. Transportation Research Record 997, Transportation Research Board, Washington, DC, 56-61.
   Google Scholar
• George, K.P., et al., 1989. Models for predicting pavement deterioration. Transportation Research Record 1215, Transportation Research Board, Washington, DC, 1–7.
   Google Scholar
• Ghahramani, Z., 2013. Bayesian non-parametrics and the probabilistic approach to modelling, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371: 20110553. doi:10.1098/rsta.2011.0553.
   PubMed Web of Science ®Google Scholar
• Gharaibeh, N., et al., 2012. Evaluation and development of pavement scores, performance models and needs estimates for the TXDOT pavement management information system–final report. Report. FHWA/TX- 12/0-6386-3. Available from: https://static.tti.tamu.edu/tti.tamu.edu/documents/0-6386-3.pdf [Accessed 10 July 2017]
   Google Scholar
• Hand, A.J., Seebaly, P.E., and Epps, J.A., 1999. Development of Performance Models Based on Department of Transportation Pavement Management System Data. Transportation Research Record 1684, Transportation Research Board, Washington, DC, 215–222.
   Google Scholar
• Highway Research Board, 1962. The AASHO road test. special Rep. No. 61A-E, National Academy of Science, National Research Council, Washington, DC.
   Google Scholar
• Hong, F., and Prozzi, J., 2006. Estimation of pavement performance deterioration using Bayesian approach. Journal of Infrastructure Systems., 12 (2), 77–86. doi:10.1061/(ASCE)1076-0342(2006)12:2(77).
   Web of Science ®Google Scholar
• Hong, F., and Prozzi, J., 2010. Roughness model accounting for heterogeneity based on In-service pavement performance data. Journal of Transportation Engineering., 136 (3), 205–213. doi: 10.1061/(ASCE)0733-947X(2010)136:3(205)
   Web of Science ®Google Scholar
• Hsu, D., 2015. Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data. Applied Energy, 160, 153–163.
   Web of Science ®Google Scholar
• Huang, Y.H., 2003. Pavement analysis and design, 2nd ed. Prentice Hall, Inc. New Jersey.
   Google Scholar
• Hudson, W.R., Haas, R., and Perrone, E., 2015. Measures of Pavement Performance must consider the Road User. In: 9th International Conference on Managing Pavement Assets, 18–21 May 2015 Alexandria, VA.
   Google Scholar
• Johnson, D.S., et al., 1989. Optimization by simulated annealing: An experimental evaluation, part I, graph partitioning. Journal Operations Research, 37 (6), 865–892.
   Web of Science ®Google Scholar
• Kang, C., and Ghosal, S., 2008. Clusterwise regression using dirichlet process mixtures. Statistical Science and Interdisciplinary Research, Vol. 4, Advances in Multivariate Statistical Methods, 305–325.
   Google Scholar
• Kay, R.K., Mahoney, J.P., and Jackson, N.C., 1993. The WSDOT Pavement Management System – A 1993 Update. Technical Rep. WA-RD 274.1, Washington State Dept. of Transportation, Olympia, WA.
   Google Scholar
• Khadka, M., and Paz, A., 2017a. Comprehensive clusterwise linear regression for pavement management systems. Journal of Transportation Engineering, Part B: Pavements, 143 (4), 04017014 (1–13).
   Web of Science ®Google Scholar
• Khadka, M., and Paz, A., 2017b. Estimation of optimal pavement performance models for highways. In: A. Loizos, I. Al-Qadi, and T. Scarpas, eds. 10^th international conference on the bearing capacity of roads, railways and airfields, 28–30 June 2017 London: CRC Press.
   Google Scholar
• Khatta, M.J., et al., 2008. Review of Louisiana’s pavement management system—Phase I. Transportation Research Record 2084, Transportation Research Board, Washington, DC, 18–27.
   Google Scholar
• Kodinariya, T.M., and Makwana, P.R., 2013. Review on determining number of cluster in k-means clustering. International Journal of Advanced Research in Computer Science and Management Studies, 1 (6), 90–95.
   Google Scholar
• Li, Z., 2005. A probabilistic and adaptive approach to modeling performance of pavement infrastructure. Thesis (PhD). University of Texas at Austin.
   Google Scholar
• Li, N., Hass, R., and Xie, W.C., 1997. Investigation of Relationship Between Deterministic and Probabilistic Prediction Models in Pavement Management. Transportation Research Record 1592, Transportation Research Board, Washington, DC, 70–79, doi:10.3141/1592-09.
   Google Scholar
• Luo, Z., and Chou, E.Y., 2006. Pavement Condition Prediction Using Clusterwise Regression. Transportation Research Record 1974, Transportation Research Board, Washington, DC, 70–77, doi:10.3141/1974-11.
   Google Scholar
• Luo, Z., and Yin, H., 2008. Probabilistic Analysis of Pavement Distress Ratings with the Clusterwise Regression Method. Transportation Research Record 2084, Transportation Research Board, Washington, DC, 38–46, doi:10.3141/2084-05.
   Google Scholar
• Lytton, R.L., 1987. Concepts of Pavement Performance Prediction and Modeling. In: Second North American Conference on Managing Pavements, Canada, Vol. 2, Nov. 1987, pp. 2.3–2.20.
   Google Scholar
• Madanat, S., et al., 2008. Development of Empirical-Mechanistic Pavement Performance Models using Data from the Washington State PMS Database. Research Report UCPRC-RR-2005-05, Univ. of Cal., Davis and Berkeley, Available from: http://www.dot.ca.gov/newtech/researchreports/reports/2008/ucprc-rr-2005-05.pdf [Accessed 15 December 2016].
   Google Scholar
• Marler, R., and Arora, J., 2004. Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization, 26, 369–395.
   Web of Science ®Google Scholar
• Meneses, S., and Ferreira, A., 2013. Pavement maintenance programming considering two objectives: maintenance costs and user costs. International Journal of Pavement Engineering, 14 (2), 206–221, doi:10.1080/10298436.2012.727994.
   Web of Science ®Google Scholar
• Nakamura, V.F., and Michael, H.L., 1963. Serviceability ratings of highway pavements. In: Highway research record 40, Highway Research Board, Washington, DC, 1–36.
   Google Scholar
• Nevada Department of Transportation, 2011. Pavement management system overview. material division, Carson City, Nevada.
   Google Scholar
• Park, M.W., and Kim, Y.D., 1998. A systematic procedure for setting parameters in simulated annealing algorithms. Computers & Operations Research, 25 (3), 207–217.
   Web of Science ®Google Scholar
• Paz, A., et al., 2015a. Calibration of traffic flow models using a memetic algorithm. Transportation Research Part-C: Emerging Technologies, 55, 432–443.
   Web of Science ®Google Scholar
• Paz, A., Molano, V., and Sanchez, M., 2015b. Holistic calibration of microscopic traffic flow models: methodology and real world application studies. In: N. Lagaros and M. Papadrakakis, eds. Engineering and Applied Sciences Optimization. Computational Methods in Applied Sciences, vol. 38, Cham: Springer.
   Google Scholar
• Prozzi, J.A., and Madanat, S.M., 2003. Incremental nonlinear model for predicting pavement serviceability. Journal of Transportation Engineering., 129 (6), 635–641. doi:10.1061/(ASCE)0733-947X(2003)129:6(635).
   Web of Science ®Google Scholar
• Prozzi, J. A., and Madanat, S.M., 2004. Development of pavement performance models by combining experimental and field data. Journal of Infrastructure Systems, 10 (1), 9–22.
   Google Scholar
• Rajagopal, A., 2006. Developing Pavement performance prediction models and decision trees for the City of Cincinnati. FHWA/OH-2006/14, Ohio DOT, Columbus, OH.
   Google Scholar
• R Core Team, 2015. R: A language and environment for statistical computing, Vienna: R Foundation for Statistical Computing. Available from: Error! Hyperlink reference not valid.http://www.R-project.org [Accessed 11 November 2016].
   Google Scholar
• Reza, F., Boriboonsomsin, K., and Bazlamit, S.M., 2005. Development of a Composite Pavement Performance Index. Report No. ST/SS/05-001. Department of Civil Engineering Ohio Northern University Ada, Ohio.
   Google Scholar
• Sadek, A.W., Freeman, T.E., and Demetsky, M.J., 1996. Deterioration Prediction Modeling of Virginia’s Interstate Highway System. Transportation Research Record 1524, Transportation Research Board, Washington, DC, 118–129, doi:10.3141/1524-14.
   Google Scholar
• Salama, H., Chatti, K., and Lyles, R., 2006. Effect of heavy multiple axle trucks on flexible pavement damage using In-service pavement performance data. Journal of Transportation Engineering., 132 (10), 763–770.
   Web of Science ®Google Scholar
• Saraf, C.L., and Majizzadeh, K., 1992. Distress prediction models for a network level PMS. Transportation Research Record, Transportation Research Board, Washington, DC, 1344, 38–48.
   Google Scholar
• Schmitt, R.L., Owusu-Ababio, S., and Denn, K.D., 2008. Database Development for an HMA Pavement Performance Analysis System. Report No. 07-11, University of Wisconsin-Madison. Available from: http://wisconsindot.gov/documents2/research/06-13hmadatabase-f.pdf [Accessed 15 December 2016].
   Google Scholar
• Schwarz, G., 1978. Estimating the dimension of a model. Annals of Statistics, 6 (2), 461–464.
   Web of Science ®Google Scholar
• Selim, S.Z., and Alsultan, K., 1991. A simulated annealing algorithm for the clustering problem. Pattern Recognition, 24 (10), 1003–1008.
   Web of Science ®Google Scholar
• Shekharan, A.R., 2000. Solution of pavement deterioration equations by genetic algorithms. Transportation Research Record 1699, Transportation Research Board, Washington, DC, 101–106, doi:10.3141/1699-14.
   Google Scholar
• Shoukry, S.N., Martinelli, D.R., and Reigle, J.A., 1997. Universal pavement distress evaluator based on fuzzy sets, Transportation Research Record 1592, Transportation Research Board, Washington, D.C., 180–186.
   Google Scholar
• Sun, L.X. et al., 1994. Cluster analysis by simulated annealing. Computers & Chemistry, 18 (2), 103–108.
   Google Scholar
• Terzi, S., 2006. Modeling the pavement present serviceability index of flexible highway pavements using data mining. Journal of Applied Sciences, 6 (1), 193–197.
   Google Scholar
• van der Merwe, D.W., and Engelbrecht, A.P., 2003. Data clustering using particle swarm optimization. In: IEEE Congress on Evolutionary Computation 2003 (CEC 2003), Canbella, Australia, 215–220. doi:10.1109/CEC.2003.1299577.
   Google Scholar
• Wolters, A.S., and Zimmerman, K.A., 2010. Research of Current practices in pavement performance modeling. FHWA –PA-2010-007-080307, PennDOT, Harrisburg, PA. Available from: http://ntl.bts.gov/lib/32000/32900/32969/Research_of_Current_Practices_in_Pavement_Performance_Modeling.pdf [ Accessed 12 December 2016].
   Google Scholar
• Yang, J., Lu, J.J., and Gunaratne, M., 2003. Application of neural network models for forecasting pavement crack index and pavement condition rating. Department of Civil and Environmental Engineering, University of South Florida, Florida. Available from: http://www.fdot.gov/research/completed_proj/summary_smo/fdot_bc353_13rpt.pdf [Accessed 15 November 2017].
   Google Scholar
• Zhang, W., and Durango-Cohen, P., 2014. Explaining heterogeneity in pavement deterioration: clusterwise linear regression model. Journal of Infrastructure Systems., 20 (2), 04014005. doi:10.1061/(ASCE)IS.1943-555X.0000182.
   Web of Science ®Google Scholar
• Zhang, Z., Singh, N., and Hudson, W.R., 1993. Comprehensive ranking index for flexible pavement using fuzzy sets model. Transportation Research Records 1397, Transportation Research Board, Washington, D.C., 96–102.
   Google Scholar
• Zheng, L., 2005. A probabilistic and adaptive approach to modeling performance of pavement infrastructure. Thesis (PhD). The University of Texas at Austin.
   Google Scholar